WhatA commercial software layer that transparently extends GPU VRAM using system RAM and NVMe via DMA-BUF, optimized for local AI inference workloads.
SignalDevelopers and AI enthusiasts with consumer GPUs (12-16GB VRAM) are stuck between models that don't fit in VRAM and cloud costs they don't want to pay, and the existing CPU offloading approach tanks performance by 5-10x.
Why NowLarge language models have exploded in size while consumer GPU VRAM has barely grown, creating a massive gap between what people want to run locally and what their hardware supports.
MarketMillions of local AI hobbyists and developers running models on consumer GPUs; TAM roughly $500M+ if sold as a software license; competes with llama.cpp CPU offloading and cloud inference APIs.
MoatDeep kernel-level and driver-level engineering creates high technical barriers; could build proprietary optimizations around specific hardware configurations that compound over time.
Nvidia greenboost: transparently extend GPU VRAM using system RAM/NVMeView discussion ↗ · Article ↗ · 404 pts · March 18, 2026
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